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      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.4\n",
      "MAE: 0.151\n"
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data = np.concatenate((train, test))\n",
    "\n",
    "mae, y, yhat, model= walk_forward_validation(data, 3)\n",
    "print('MAE: %.3f' % mae)\n",
    "# plot expected vs predicted\n",
    "pyplot.plot(y, label='Expected')\n",
    "pyplot.plot(yhat, label='Predicted')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd \n",
    "import timeseries as ts\n",
    "from pandas import DataFrame\n",
    "import numpy as np\n",
    "from numpy import asarray\n",
    "from pandas import read_csv\n",
    "from pandas import DataFrame\n",
    "from pandas import concat\n",
    "from sklearn.ensemble import RandomForestRegressor\n",
    "from sklearn.metrics import mean_absolute_error\n",
    "from matplotlib import pyplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def series_to_supervised(data, n_in=1, n_out=1, dropnan=True):\n",
    "\tn_vars = 1 #if type(data) is list else data.shape[1]\n",
    "\tdf = DataFrame(data)\n",
    "\tcols = list()\n",
    "\t# input sequence (t-n, ... t-1)\n",
    "\tfor i in range(n_in, 0, -1):\n",
    "\t\tcols.append(df.shift(i))\n",
    "\t# forecast sequence (t, t+1, ... t+n)\n",
    "\tfor i in range(0, n_out):\n",
    "\t\tcols.append(df.shift(-i))\n",
    "\t# put it all together\n",
    "\tagg = pd.concat(cols, axis=1)\n",
    "\t# drop rows with NaN values\n",
    "\tif dropnan:\n",
    "\t\tagg.dropna(inplace=True)\n",
    "\treturn agg.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Fecha\n2017-01-01      123704\n2017-01-02    21209771\n2017-01-03    26706072\n2017-01-04    29734324\n2017-01-05    58392417\nName: Ventas, dtype: int64\nModel Fitting Time: 0.03869891166687012\n                                SARIMAX Results                                \n===============================================================================\nDep. Variable:                  Ventas   No. Observations:                  912\nModel:             SARIMAX(0, 1, 0, 7)   Log Likelihood                1150.048\nDate:                 Tue, 01 Dec 2020   AIC                          -2298.096\nTime:                         19:02:40   BIC                          -2293.289\nSample:                     01-01-2017   HQIC                         -2296.260\n                          - 07-01-2019                                         \nCovariance Type:                   opg                                         \n==============================================================================\n                 coef    std err          z      P>|z|      [0.025      0.975]\n------------------------------------------------------------------------------\nsigma2         0.0046   7.79e-05     59.199      0.000       0.004       0.005\n===================================================================================\nLjung-Box (L1) (Q):                  27.42   Jarque-Bera (JB):              6897.83\nProb(Q):                              0.00   Prob(JB):                         0.00\nHeteroskedasticity (H):               5.18   Skew:                             0.03\nProb(H) (two-sided):                  0.00   Kurtosis:                        16.52\n===================================================================================\n\nWarnings:\n[1] Covariance matrix calculated using the outer product of gradients (complex-step).\n"
     ]
    }
   ],
   "source": [
    "\n",
    "sarima0000107 = ts.TimeSeries((0, 0, 0), (0, 1, 0, 7))\n",
    "\n",
    "df = sarima0000107.train_data.to_numpy()\n",
    "test_df = sarima0000107.test_data.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "train = series_to_supervised(df, n_out = 1)\n",
    "test = series_to_supervised(test_df, n_out = 1)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "# split a univariate dataset into train/test sets\n",
    "def train_test_split(data, n_test):\n",
    "\treturn data[:-n_test, :], data[-n_test:, :]\n",
    "\n",
    "# fit an random forest model and make a one step prediction\n",
    "def random_forest_forecast(train, testX, estimators = 1000):\n",
    "\t# transform list into array\n",
    "\ttrain = asarray(train)\n",
    "\t# split into input and output columns\n",
    "\ttrainX, trainy = train[:, :-1], train[:, -1]\n",
    "\t# fit model\n",
    "\tmodel = RandomForestRegressor(n_estimators=estimators)\n",
    "\tmodel.fit(trainX, trainy)\n",
    "\t# make a one-step prediction\n",
    "\tyhat = model.predict([testX])\n",
    "\treturn yhat[0], model\n",
    " \n",
    "# walk-forward validation for univariate data\n",
    "def walk_forward_validation(data, n_test, estimators = 1000):\n",
    "\tpredictions = list()\n",
    "\t# split dataset\n",
    "\tmodel = {}\n",
    "\ttrain, test = train_test_split(data, n_test)\n",
    "\t# seed history with training dataset\n",
    "\thistory = [x for x in train]\n",
    "\t# step over each time-step in the test set\n",
    "\tfor i in range(len(test)):\n",
    "\t\t# split test row into input and output columns\n",
    "\t\ttestX, testy = test[i, :-1], test[i, -1]\n",
    "\t\t# fit model on history and make a prediction\n",
    "\t\tyhat, model = random_forest_forecast(history, testX)\n",
    "\t\t# store forecast in list of predictions\n",
    "\t\tpredictions.append(yhat)\n",
    "\t\t# add actual observation to history for the next loop\n",
    "\t\thistory.append(test[i])\n",
    "\t\t# summarize progress\n",
    "\t\tprint('>expected=%.1f, predicted=%.1f' % (testy, yhat))\n",
    "\t# estimate prediction error\n",
    "\terror = mean_absolute_error(test[:, -1], predictions)\n",
    "\treturn error, test[:, -1], predictions, model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.2\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.0, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.0, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.5, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.6, predicted=0.1\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.7, predicted=0.1\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.8, predicted=0.2\n",
      ">expected=0.7, predicted=0.4\n",
      ">expected=0.3, predicted=0.5\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.4, predicted=0.4\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.5, predicted=0.3\n",
      ">expected=0.2, predicted=0.4\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.5, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.5\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.3, predicted=0.5\n",
      ">expected=0.6, predicted=0.5\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.7, predicted=0.1\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.5\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.1\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.3\n",
      ">expected=0.7, predicted=0.5\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.8, predicted=0.2\n",
      ">expected=0.8, predicted=0.4\n",
      ">expected=0.2, predicted=0.7\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.3, predicted=0.5\n",
      ">expected=0.6, predicted=0.3\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.5, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.4\n",
      ">expected=1.0, predicted=0.2\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.1, predicted=0.4\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.6, predicted=0.3\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.3, predicted=0.5\n",
      ">expected=0.7, predicted=0.3\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.4\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.7, predicted=0.4\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.5, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.6, predicted=0.3\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.4, predicted=0.4\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.1, predicted=0.5\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.6\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.8, predicted=0.2\n",
      ">expected=0.5, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.1\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.5\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.8, predicted=0.3\n",
      ">expected=0.5, predicted=0.7\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.5\n",
      ">expected=0.6, predicted=0.4\n",
      ">expected=0.2, predicted=0.4\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.8, predicted=0.2\n",
      ">expected=0.6, predicted=0.5\n",
      ">expected=0.2, predicted=0.4\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.4\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.8, predicted=0.3\n",
      ">expected=0.6, predicted=0.4\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.5\n",
      ">expected=0.6, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.0\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.3\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.8, predicted=0.4\n",
      ">expected=0.2, predicted=0.5\n",
      ">expected=0.0, predicted=0.3\n",
      ">expected=0.1, predicted=0.0\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.5, predicted=0.2\n",
      ">expected=0.7, predicted=0.3\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.3\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.1, predicted=0.5\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.7, predicted=0.6\n",
      ">expected=0.6, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.1, predicted=0.2\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.2, predicted=0.1\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.6, predicted=0.4\n",
      ">expected=0.3, predicted=0.4\n",
      ">expected=0.2, predicted=0.4\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.2, predicted=0.3\n",
      ">expected=0.3, predicted=0.1\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.8, predicted=0.2\n",
      ">expected=0.6, predicted=0.6\n",
      ">expected=0.2, predicted=0.4\n",
      ">expected=0.2, predicted=0.2\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.5, predicted=0.2\n",
      ">expected=0.5, predicted=0.2\n",
      ">expected=0.9, predicted=0.2\n",
      ">expected=0.5, predicted=0.6\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.6\n",
      ">expected=0.3, predicted=0.3\n",
      ">expected=0.1, predicted=0.3\n",
      ">expected=0.4, predicted=0.3\n",
      ">expected=0.7, predicted=0.2\n",
      ">expected=0.5, predicted=0.5\n",
      ">expected=0.3, predicted=0.2\n",
      ">expected=0.4, predicted=0.2\n",
      ">expected=0.2, predicted=0.4\n",
      "MAE: 0.091\n"
     ]
    },
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "data = np.concatenate((train, test))\n",
    "# evaluate\n",
    "mae, y, yhat, model = walk_forward_validation(data, len(train))\n",
    "print('MAE: %.3f' % mae)\n",
    "# plot expected vs predicted\n",
    "pyplot.plot(y, label='Expected')\n",
    "pyplot.plot(yhat, label='Predicted')\n",
    "pyplot.legend()\n",
    "pyplot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "RandomForestRegressor(n_estimators=1000)"
      ]
     },
     "metadata": {},
     "execution_count": 25
    }
   ],
   "source": [
    "model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    },
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "RMSE:  0.17093249457883067\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "[array([0.24540073]),\n",
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       " array([0.28312375]),\n",
       " array([0.14373574]),\n",
       " array([0.20303263]),\n",
       " array([0.23544035]),\n",
       " array([0.41564889]),\n",
       " array([0.60521203]),\n",
       " array([0.5806379]),\n",
       " array([0.29157241]),\n",
       " array([0.15766787]),\n",
       " array([0.21047425]),\n",
       " array([0.32794492]),\n",
       " array([0.41928625]),\n",
       " array([0.60336591]),\n",
       " array([0.50971535]),\n",
       " array([0.26496464]),\n",
       " array([0.15852666]),\n",
       " array([0.22372405]),\n",
       " array([0.25781627]),\n",
       " array([0.49854348]),\n",
       " array([0.47164214]),\n",
       " array([0.47354692]),\n",
       " array([0.38124155]),\n",
       " array([0.15074973]),\n",
       " array([0.15359275]),\n",
       " array([0.13856525]),\n",
       " array([0.24324422]),\n",
       " array([0.50140958]),\n",
       " array([0.49330295]),\n",
       " array([0.24111029]),\n",
       " array([0.16651787]),\n",
       " array([0.15938624]),\n",
       " array([0.25339893]),\n",
       " array([0.22985191]),\n",
       " array([0.65417709]),\n",
       " array([0.3122757]),\n",
       " array([0.11163027]),\n",
       " array([0.09283253]),\n",
       " array([0.2062269]),\n",
       " array([0.21904991]),\n",
       " array([0.39180395]),\n",
       " array([0.52188157]),\n",
       " array([0.46959044]),\n",
       " array([0.28465923]),\n",
       " array([0.19161643]),\n",
       " array([0.16297521]),\n",
       " array([0.23621344]),\n",
       " array([0.27299092]),\n",
       " array([0.50085796]),\n",
       " array([0.42544628]),\n",
       " array([0.27576364]),\n",
       " array([0.2916315]),\n",
       " array([0.13865685]),\n",
       " array([0.16527149]),\n",
       " array([0.20792456]),\n",
       " array([0.67793837]),\n",
       " array([0.54930258]),\n",
       " array([0.2313549]),\n",
       " array([0.18158569]),\n",
       " array([0.24070815]),\n",
       " array([0.22898251]),\n",
       " array([0.17900342]),\n",
       " array([0.11678567]),\n",
       " array([0.33083899]),\n",
       " array([0.3336]),\n",
       " array([0.15803719]),\n",
       " array([0.24582109]),\n",
       " array([0.26254691]),\n",
       " array([0.36125654]),\n",
       " array([0.54775773]),\n",
       " array([0.54487384]),\n",
       " array([0.31283904]),\n",
       " array([0.27888596]),\n",
       " array([0.20969194]),\n",
       " array([0.28642197]),\n",
       " array([0.3734934]),\n",
       " array([0.56136971]),\n",
       " array([0.49301984]),\n",
       " array([0.32056136]),\n",
       " array([0.21921411]),\n",
       " array([0.23333653]),\n",
       " array([0.28310817]),\n",
       " array([0.49359808]),\n",
       " array([0.60423042]),\n",
       " array([0.57094683]),\n",
       " array([0.28907975]),\n",
       " array([0.23382879]),\n",
       " array([0.32194436]),\n",
       " array([0.36926339]),\n",
       " array([0.41763874]),\n",
       " array([0.63771686]),\n",
       " array([0.55488801]),\n",
       " array([0.25157243]),\n",
       " array([0.51505924]),\n",
       " array([0.28052098]),\n",
       " array([0.19322293]),\n",
       " array([0.39812196]),\n",
       " array([0.52834614]),\n",
       " array([0.54190933]),\n",
       " array([0.26923481]),\n",
       " array([0.33416502]),\n",
       " array([0.37732447])]"
      ]
     },
     "metadata": {},
     "execution_count": 26
    }
   ],
   "source": [
    "predictions = list()\n",
    "testC = test[:, :-1]\n",
    "for i in range(len(testC)):\n",
    "    prediction = model.predict([testC[i]])\n",
    "    predictions.append(prediction)\n",
    "\n",
    "# plot expected vs predicted\n",
    "pyplot.plot(testC, label='Expected')\n",
    "pyplot.plot(predictions, label='Predicted')\n",
    "pyplot.legend()\n",
    "pyplot.show()\n",
    "\n",
    "print(\"RMSE: \", np.sqrt(((predictions - testC) ** 2).mean()))\n",
    "\n",
    "predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ]
}